Type: Bioinformatics · Data analysis · No wet lab
Project description
This project explores how large language models can support interpretation of complex proteomics datasets by generating biologically relevant and testable hypotheses. Students will develop structured workflows to guide hypothesis generation from noisy proteomics data and assess the robustness of the results.
Example project aim
To use a large language model to generate and prioritise mechanistic hypotheses from proteomics data obtained from lung tissue exposed to air pollution, and to evaluate these hypotheses using independent pathway resources.
Project description
This project explores how large language models can support interpretation of complex proteomics datasets by generating biologically relevant and testable hypotheses. Students will develop structured workflows to guide hypothesis generation from noisy proteomics data and assess the robustness of the results.
Example project aim
To use a large language model to generate and prioritise mechanistic hypotheses from proteomics data obtained from lung tissue exposed to air pollution, and to evaluate these hypotheses using independent pathway resources.